Is it possible to use AI to denoise noised or 'dirty' documents?
It is possible remove noise from signal if what is considered noise is distinguishable from what is considered signal. This is true of documents, images, audio, or any other type of information.

If dirty means wrong content, then probably not. If dirty means mispellings, then probably yes. If dirty means tilted by a few degrees and containing edge artifacts around the border from scanning, then definitely yes.

Most OCR software, such as the open source program Tesseract, can produce fairly accurate text even with some of the above issues in the document.

If the documents have noise gradients, they may be difficult to OCR. That is different than a value or intensity gradient, which can be normalized. Using AI to remove noise gradients from documents is a reasonable plan, but there is a limit to any kind of intelligence. A scrambled egg cannot be reconstituted.

A simple test of feasibility is whether a human that has no domain knowledge about the subject of the document can make out the text. If they cannot, then a computer will not be able to either, unless the vocabulary is labeled with the probability of each word appearing in that subject matter and the OCR system is trained that way.

However, if the noise is on a gray scale so that the darkness of the type is greater than one or two standard deviations of the noise distribution, then the document can be divided by a parameterizable grid with parameterizable brightness and contrast. This can become the model used in conjunction with a basic feed forward network (mini-batched multilayer perceptron) and the OCR software. The idea in this architecture would be to converge on a network behavior that, for the documents in the example set that have been hand adjusted so that the OCR produces good output, could then be used for many more documents with similar noise gradients.

The inputs for the artificial network would be statistics about the value distribution for specific sections on a specific page of the document and the labels will be the manually created instructions for gradient based contract and value correction of the document so the OCR proceeds with few errors. Once the network is trained, its output could ideally drive the gradient based corrections. It would probably work if three things are in order.


The noise doesn't completely destroy the availability of the text so that an average human could not make out the words.
The training of the network converges to a degree that the OCR software works across the pages for most of the documents.
The characteristics of the noise gradients remain consistent, meaning that the training examples are representative of the documents that will be processed later.